{"id":"W4404874504","doi":"10.1016/j.compag.2024.109722","title":"Corrigendum to “A chlorophyll-constrained semi-empirical model for estimating leaf area index using a red-edge vegetation index” [Comput. Electron. Agric. 220 (2024) 108891]","year":2024,"lang":"en","type":"erratum","venue":"Computers and Electronics in Agriculture","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Index (typography); Vegetation Index; Vegetation (pathology); Red edge; Enhanced Data Rates for GSM Evolution; Leaf area index; Chlorophyll; Environmental science; Mathematics; Remote sensing; Geography; Computer science; Botany; Normalized Difference Vegetation Index; Biology; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002245203,0.002990504,0.002761894,0.003898791,0.002893685,0.002930257,0.00432539,0.004850328,0.1751896],"category_scores_gemma":[0.02576705,0.001454904,0.002363159,0.003161229,0.00145015,0.002422622,0.002390451,0.005833555,0.138072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0041013,"about_ca_system_score_gemma":0.002653513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04707365,"about_ca_topic_score_gemma":0.07213856,"domain_scores_codex":[0.9971578,0.0005183302,0.0003926253,0.0004712722,0.001265776,0.0001942445],"domain_scores_gemma":[0.9841986,0.002572528,0.0003926449,0.0009099331,0.0114176,0.0005087052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001576177,0.000006792388,0.00002868722,0.00004957945,0.000006952384,0.00006646414,0.000005924064,0.0001139077,0.00005793402,0.0004132076,0.9955353,0.003699466],"study_design_scores_gemma":[0.00002677001,0.00002229256,0.0007853599,0.0001453146,0.00002736303,0.000182281,0.00003111197,0.001034208,0.0004302508,0.002073487,0.995194,0.00004756449],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001882084,0.002060528,0.004651901,0.03216379,0.9407798,0.00008269156,0.003209737,0.001142788,0.01572046],"genre_scores_gemma":[0.005678262,0.006076626,0.01096984,0.04089858,0.1391584,0.0003136987,0.009402278,0.003150657,0.7843516],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1751896,"threshold_uncertainty_score":0.5860677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04748772390974634,"score_gpt":0.2593135479303439,"score_spread":0.2118258240205976,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}